The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset

Barnes, Dan; Gadd, Matthew; Murcutt, Paul; Newman, Paul; Posner, Ingmar

doi:10.1109/icra40945.2020.9196884

Cited by 304 publications

(196 citation statements)

References 16 publications

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“…As we know, deep learning is data-driven, and quality data are crucial for neural network training. At present, the public datasets containing radar information mainly include Nuscence [ 35 ], CRUW [ 36 ] and Oxford Radar Robotcar Dataset [ 37 ]. However, these datasets contain only 2D radar point information or radiofrequency (RF) images of the radar.…”

Section: Resultsmentioning

confidence: 99%

Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Bai

Zheng

et al. 2021

Sensors

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Automotive millimeter-wave (MMW) radar is essential in autonomous vehicles due to its robustness in all weather conditions. Traditional commercial automotive radars are limited by their resolution, which makes the object classification task difficult. Thus, the concept of a new generation of four-dimensional (4D) imaging radar was proposed. It has high azimuth and elevation resolution and contains Doppler information to produce a high-quality point cloud. In this paper, we propose an object classification network named Radar Transformer. The algorithm takes the attention mechanism as the core and adopts the combination of vector attention and scalar attention to make full use of the spatial information, Doppler information, and reflection intensity information of the radar point cloud to realize the deep fusion of local attention features and global attention features. We generated an imaging radar classification dataset and completed manual annotation. The experimental results show that our proposed method achieved an overall classification accuracy of 94.9%, which is more suitable for processing radar point clouds than the popular deep learning frameworks and shows promising performance.

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Section: Resultsmentioning

confidence: 99%

Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Bai

Zheng

et al. 2021

Sensors

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“…We run six epochs with the Adam optimizer (Kingma and Ba, 2015) and a decayed learning rate from 0.001. We conduct the experiments on two public datasets, the Oxford Radar RobotCar (RobotCar) dataset (Maddern et al, 2017;Barnes et al, 2020a) and the Multimodal Range (MulRan) dataset (Kim et al, 2020). Both these datasets use the Navtech FMCW radar but the 3D lidar sensors use different ones, double Velodyne HDL-32E and one Ouster OS1-64.…”

Section: Implementation and Experimental Setupmentioning

confidence: 99%

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Yin

Wang

et al. 2021

Front. Robot. AI

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Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditions. In this paper, a heterogeneous measurement based framework is proposed for long-term place recognition, which retrieves the query radar scans from the existing lidar (Light Detection and Ranging) maps. To achieve this, a deep neural network is built with joint training in the learning stage, and then in the testing stage, shared embeddings of radar and lidar are extracted for heterogeneous place recognition. To validate the effectiveness of the proposed method, we conducted tests and generalization experiments on the multi-session public datasets and compared them to other competitive methods. The experimental results indicate that our model is able to perform multiple place recognitions: lidar-to-lidar (L2L), radar-to-radar (R2R), and radar-to-lidar (R2L), while the learned model is trained only once. We also release the source code publicly: https://github.com/ZJUYH/radar-to-lidar-place-recognition.

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“…From the beginning of 2019-current, more datasets with radar information are being published [ 41 , 42 , 43 , 44 , 52 , 98 , 170 , 171 ], therefore enabling more research to be realized using high-resolution radars and enhancing the development of multimodal fusion networks for autonomous driving using deep neural network architectures. For example, the authors of [ 170 ] developed the Oxford radar dataset for autonomous vehicle and mobile robot applications, benefitting from the FMCW radar sensor’s capabilities in adverse weather conditions. The large-scale dataset is an upgrade to their earlier release [ 42 ], incorporating one mm-wave radar and two additional Velodyne 3D Lidars and recorded for over 280 km of urban driving at Central Oxford under different weather, traffic, and lighting conditions.…”

Section: Datasetsmentioning

confidence: 99%

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Abdu

Zhang

et al. 2021

Sensors

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The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

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The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset

Cited by 304 publications

References 16 publications

Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

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